@article{FernandezTNN2013,
author = "Francisco Fernandez-Navarro and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Xin Yao",
abstract = "In this paper, two neural network threshold ensemble models are proposed for ordinal regression problems. For the first ensemble method, the thresholds are fixed a priori and are not modified during training. The second one considers the thresholds of each member of the ensemble as free parameters, allowing their modification during the training process. This is achieved through a reformulation of these tunable thresholds, which avoids the constraints they must fulfil for the ordinal regression problem. During training, diversity existed in the different projections generated by each member is taken into account for the parameter updating. This diversity is promoted in an explicit way by using a 'diversity-encouraging' error function, extending the well-known Negative Correlation Learning framework to the area of ordinal regression, and inheriting many of its good properties. Experimental results demonstrate that the proposed algorithms can achieve competitive generalization performance when considering four ordinal regression metrics.",
awards = "JCR (2013): 4.370 (category COMPUTER SCIENCE, THEORY {\&} METHODS, position 2/102 Q1)",
comments = "JCR (2013): 4.370 (category COMPUTER SCIENCE, THEORY {\&} METHODS, position 2/102 Q1)",
doi = "10.1109/TNNLS.2013.2268279",
journal = "IEEE Transactions on Neural Networks and Learning Systems",
keywords = "Negative correlation learning, neural network ensembles, ordinal regression, threshold methods",
month = "November",
note = "JCR (2013): 4.370 (category COMPUTER SCIENCE, THEORY {\&} METHODS, position 2/102 Q1)",
number = "11",
pages = "1836--1849",
title = "{N}egative {C}orrelation {E}nsemble {L}earning for {O}rdinal {R}egression",
url = "http://dx.doi.org/10.1109/TNNLS.2013.2268279",
volume = "24",
year = "2013",
}